Caspio vs. Google BigQuery

Contact forms are incredibly easy to build and customize, as are most other kinds of more involved forms. Forms allow html input and can be designed in a number of ways. It's helpful if you know some HTML. JavaScript knowledge can add advanced options, but is not necessary for the most common uses. The company used to call itself "No-Code," but has changed to say "Low Code," which is much more accurate.

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Event-based data can be captured seamlessly from our data layers (and exported to Google BigQuery). When events like page-views, clicks, add-to-cart are tracked, Google BigQuery can help efficiently with running queries to observe patterns in user behaviour. That intermediate step of trying to "untangle" event data is resolved by Google BigQuery. A scenario where it could possibly be less appropriate is when analysing "granular" details (like small changes to a database happening very frequently).

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• Caspio is an online database platform. It allows one to create multiple tables and views to be stored via the web. Tables can be configured to not only hold data but also to hold files like Word docs and images.
• Caspio is great at creating online forms and reports. It allows the user to create forms to capture, update data, whether it's password protected or just a simple form.
• As a database platform, one can create online tables and forms linking multiple tables and views to each other. I would say it is the Microsoft Access of the online platforms.
• The system is very easy. Wizards allow one to create forms, tables, and datapages. Since the platform is online, you may access your database anywhere.

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• Realtime integration with Google Sheets.
• GSheet data can be linked to a BigQuery table and the data in that sheet is ingested in realtime into BigQuery. It's a live 'sync' which means it supports insertions, deletions, and alterations. The only limitation here is the schema'; this remains static once the table is created.
• Seamless integration with other GCP products.
• A simple pipeline might look like this:-
• GForms -> GSheets -> BigQuery -> Looker
• It all links up really well and with ease.
• One instance holds many projects.
• Separating data into datamarts or datameshes is really easy in BigQuery, since one BigQuery instance can hold multiple projects; which are isolated collections of datasets.

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• Pricing for additional storage could be a bit more competitive
• The builder wizard is helpful, however there could be a tutorial for first time users
• Calendar pdf exports would be usefull

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• Please expand the availability of documentation, tutorials, and community forums to provide developers with comprehensive support and guidance on using Google BigQuery effectively for their projects.
• If possible, simplify the pricing model and provide clearer cost breakdowns to help users understand and plan for expenses when using Google BigQuery. Also, some cost reduction is welcome.
• It still misses the process of importing data into Google BigQuery. Probably, by improving compatibility with different data formats and sources and reducing the complexity of data ingestion workflows, it can be made to work.

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We are committed to Caspio Bridge. We could migrate to another product, but we would not be able to do so quickly. I estimate that we would need about a year (maybe more) to convert all of our forms and processes if we were to switch to some other product

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We have to use this product as its a 3rd party supplier choice to utilise this product for their data side backend so will not be likely we will move away from this product in the future unless the 3rd party supplier decides to change data vendors.

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I think overall it is easy to use. I haven't done anything from the development side but an more of an end user of reporting tables built in Google BigQuery. I connect data visualization tools like Tableau or Power BI to the BigQuery reporting tables to analyze trends and create complex dashboards.

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I have never had any significant issues with Google Big Query. It always seems to be up and running properly when I need it. I cannot recall any times where I received any kind of application errors or unplanned outages. If there were any they were resolved quickly by my IT team so I didn't notice them.

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I think Google Big Query's performance is in the acceptable range. Sometimes larger datasets are somewhat sluggish to load but for most of our applications it performs at a reasonable speed. We do have some reports that include a lot of complex calculations and others that run on granular store level data that so sometimes take a bit longer to load which can be frustrating.

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I've had some instances where my team and I discovered a bug in Caspio. We alerted the company, and the bug was fixed in the next regular update of the software. The few times that I've had to contact support, it was a pretty good experience. They're not terribly quick to respond and take action, but they don't ignore you, either.

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BigQuery can be difficult to support because it is so solid as a product. Many of the issues you will see are related to your own data sets, however you may see issues importing data and managing jobs. If this occurs, it can be a challenge to get to speak to the correct person who can help you.

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GroupHigh is so frustrating to use. It is constantly changing the way you can search for bloggers, and we can never find exactly what we are looking for

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PowerBI can connect to GA4 for example but the data processing is more complicated and it takes longer to create dashboards. Azure is great once the data import has been configured but it's not an easy task for small businesses as it is with BigQuery.

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None so far. Very satisfied with the transparency on contract terms and pricing model.

We have continued to expand out use of Google Big Query over the years. I'd say its flexibility and scalability is actually quite good. It also integrates well with other tools like Tableau and Power BI. It has served the needs of multiple data sources across multiple departments within my company.

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Google Support has kindly provide individual support and consultants to assist with the integration work. In the circumstance where the consultants are not present to support with the work, Google Support Helpline will always be available to answer to the queries without having to wait for more than 3 days.

• Positive impact of easy of adding new varieties.
• Positive impact of having new emails added directly to email list through Zapier.
• Positive impact on increasing productivity of our staff.

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• Previously, running complex queries on our on-premise data warehouse could take hours. Google BigQuery processes the same queries in minutes. We estimate it saves our team at least 25% of their time.
• We can target our marketing campaigns very easily and understand our customer behaviour. It lets us personalize marketing campaigns and product recommendations and experience at least a 20% improvement in overall campaign performance.
• Now, we only pay for the resources we use. Saved $1 million annually on data infrastructure and data storage costs compared to our previous solution.

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